#CrewAI Dify
143 related articles
·3 min
AI Large Language Model Learning Roadmap from Scratch: From Beginner to Enterprise-Level Practice
A systematic AI LLM learning roadmap from scratch, covering Python basics, Prompt Engineering, RAG, Agent development, and enterprise-level projects.
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·3 min
The Essential Difference Between Agent Skills and MCP: Process Layer vs. Capability Layer
Deep dive into the essential difference between Skill and MCP in AI Agent development. Skill handles the process layer for codifying workflows; MCP handles the capability layer for connecting external systems.
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·3 min
Core Analysis of Agent Skill: Progressive Disclosure & Middleware Practical Guide
Deep dive into Agent Skill's core design—Progressive Disclosure—with detailed middleware and dynamic tool implementation, Multi-Agent comparison, and practical tips.
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·4 min
Is the 748-Episode AI Large Language Model Tutorial on Bilibili Worth Watching? A Full Breakdown of Content Structure and Study Tips
In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.
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·3 min
MCP vs Skills: Understanding the Key to AI Testing Efficiency
Understand the key differences between MCP (Model Context Protocol) and Skills through practical analogies and real testing scenarios to boost AI-driven test automation efficiency.
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·3 min
Coze Workflow Development Tutorial: A Complete Guide to Building AI Apps with Zero Code
Complete guide to Coze workflow development covering Agent building, node orchestration, plugin systems, API integration, and a Coze vs Dify comparison.
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·4 min
Harness Engineering in Practice: Taming AI Agents with Hermes Agent
Deep dive into Harness Engineering: using the open-source Hermes Agent framework's four-layer memory system and Skill evolution to build controllable, evolvable AI agents.
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·3 min
The Complete Guide to OpenAI Codex CLI: From Installation and Configuration to Enterprise-Level Practice
In-depth guide to OpenAI Codex CLI: covering installation, agents.md design, multi-agent collaboration, MCP protocol integration, and a RAG customer service project.
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·3 min
Building a DeepSeek Agent from Scratch: Principles, Token Optimization, and Bootstrapped Development
Learn how to build a DeepSeek V3 AI Agent from scratch with zero dependencies, covering Agent loop mechanics, token optimization, cache hit strategies, and bootstrapped development.
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·2 min
Agent Middleware: Adding Interceptors to Model Calls
Learn how AI Agent middleware works through two practical examples — logging and security checks. Master the Observer and Guardian design patterns to build extensible, production-grade Agents.
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·3 min
Harness Engineering in Practice: Building an Enterprise E-Commerce System with Claude Code — A Complete Walkthrough
A deep dive into Harness Engineering for AI programming, from concept to implementation. Build an enterprise Java e-commerce system using Claude Code with Skill-driven AI development pipelines.
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·3 min
Loopcraft Explained: Loop-Stacking Architecture Design in AI Agent Development
Deep dive into Loopcraft loop-stacking architecture for AI Agent development, covering retry, self-validation, and meta-learning loops to boost reliability.
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·3 min
A Three-Month Roadmap to LLM Development: A Deep Dive into the Learning Path from Zero to Freelancing
A deep dive into the three-step LLM development learning path: from prompt engineering and RAG knowledge bases to AI Agent development, with realistic timelines for beginners and experienced developers.
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·3 min
AI Large Language Model Learning Roadmap: A Systematic Path from Zero to Project Implementation
A detailed AI LLM learning roadmap covering Transformer architecture, Prompt Engineering, RAG, Agent development, model fine-tuning & deployment, with enterprise project guides.
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·3 min
AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning
Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.
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·4 min
AI Agent Development: A Complete 6-Week Systematic Learning Roadmap
A 6-week systematic learning roadmap for AI Agent development, covering core architecture, ReAct principles, multi-agent collaboration, RAG integration, and deployment.
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·3 min
Hermes Desktop Officially Launched: A Forever-Free, Self-Evolving AI Agent for Your Desktop
Hermes Desktop is now available for Windows, macOS, and Linux. This MIT-licensed AI Agent features persistent memory, self-evolution, skill management, and multi-platform integration — completely free.
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·4 min
Superpowers: Installing Work Standards for Your AI Coding Assistant
How the Superpowers methodology constrains AI coding assistants through requirement clarification, task decomposition, TDD, and verification loops — with setup tips for Trae.
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·3 min
Building AI Agents from Scratch: A Complete Beginner's Guide
Build AI Agents with zero coding experience! Learn prompt engineering, RAG knowledge bases, and workflow orchestration using no-code platforms like Coze and Dify, plus real monetization paths.
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·3 min
Getting Started with AI Agents: A Complete Learning Path from Zero to Production
A comprehensive guide to AI Agent architecture covering ReAct paradigm, multi-agent collaboration, RAG integration, and the planning-memory-tools framework, with a complete learning path from concepts to production deployment.
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